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In multi-agent games, the complexity of the environment can grow exponentially as the number of agents increases, so it is particularly challenging to learn good policies when the agent population is large.
An overview of evolutionary algorithms for parameter optimization
Thomas Bäck and Hans-Paul Schwefel · 1993
Earlier work this paper cites.
Learning and development in neural networks: The importance of starting small
Jeffrey L Elman · 1993
Earlier work this paper cites.
Markov games as a framework for multi-agent reinforcement learning
Michael L Littman · 1994
Earlier work this paper cites.
Multi-agent reinforcement learning: a critical survey
Yoav Shoham, Rob Powers, and Trond Grenager · 2003
Earlier work this paper cites.
Cooperative multi-agent learning: The state of the art
Liviu Panait and Sean Luke · 2005
Earlier work this paper cites.
Approximate strategic reasoning through hierarchical reduction of large symmetric games
Michael P Wellman, Daniel M Reeves, Kevin M Lochner, Shih-Fen Cheng, and Rahul Suri · 2005
Earlier work this paper cites.
Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
Earlier work this paper cites.
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Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
Earlier work this paper cites.
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David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller · 2014
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Matthew Hausknecht and Peter Stone · 2015
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Jakob Foerster, Ioannis Alexandros Assael, Nando de Freitas, and Shimon Whiteson · 2016
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Opponent modeling in deep reinforcement learning
He He, Jordan Boyd-Graber, Kevin Kwok, and Hal Daumé III · 2016
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End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
Earlier work this paper cites.
Learning multiagent communication with backpropagation
Sainbayar Sukhbaatar, Rob Fergus, et al · 2016
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Training agent for first-person shooter game with actor-critic curriculum learning
Yuxin Wu and Yuandong Tian · 2016
Earlier work this paper cites.
One-shot imitation learning
Yan Duan, Marcin Andrychowicz, Bradly Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, and Wojciech Zaremba · 2017
Earlier work this paper cites.
Reverse curriculum generation for reinforcement learning
Carlos Florensa, David Held, Markus Wulfmeier, Michael Zhang, and Pieter Abbeel · 2017
Earlier work this paper cites.
Population based training of neural networks
Max Jaderberg, Valentin Dalibard, Simon Osindero, Wojciech M Czarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, Tim Green, Iain Dunning, Karen Simonyan, et al · 2017
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Progressive growing of gans for improved quality, stability, and variation
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Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, and Igor Mordatch · 2017
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Multiagent bidirectionally-coordinated nets for learning to play starcraft combat games
Peng Peng, Quan Yuan, Ying Wen, Yaodong Yang, Zhenkun Tang, Haitao Long, and Jun Wang · 2017
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Cassl: Curriculum accelerated self-supervised learning
Adithyavairavan Murali, Lerrel Pinto, Dhiraj Gandhi, and Abhinav Gupta · 2018
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Edoardo Conti, Vashisht Madhavan, Felipe Petroski Such, Joel Lehman, Kenneth Stanley, and Jeff Clune · 2018
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Non-local neural networks
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Yi Wu, Yuxin Wu, Georgia Gkioxari, and Yuandong Tian · 2018
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Mean field multi-agent reinforcement learning
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Vinicius Zambaldi, David Raposo, Adam Santoro, Victor Bapst, Yujia Li, Igor Babuschkin, Karl Tuyls, David Reichert, Timothy Lillicrap, Edward Lockhart, et al · 2018
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Actor-attention-critic for multi-agent reinforcement learning
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Iterated deep reinforcement learning in games: history-aware training for improved stability
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Visual semantic navigation using scene priors
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